Adrenomedullin in pulmonary hypertension.

Adrenomedullin (AM) exerts strong pulmonary vasodilatory effects. These effects are mediated in part by nitric oxide. Plasma AM levels are increased in patients with pulmonary hypertension and correlate with disease severity and poor outcomes.

Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.

In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfer learning to address the problem of insufficient sample sizes in neuroimaging. Viewing subjects, time, and features as dimensions, brain activation and dynamic functional connectivity data can be treated as high-order heterogeneous data with heterogeneity arising from distinct feature space.

Effect of Translation-Enhancing Nascent SKIK Peptide on the Arrest Peptides Containing Consecutive Proline.

Ribosome arrest peptides (RAPs) such as the SecM arrest peptide (SecM AP: FSTPVWISQAQGIRAGP) and WPPP with consecutive Pro residues are known to induce translational stalling in . We demonstrate that the translation-enhancing SKIK peptide tag, which consists of four amino acid residues Ser-Lys-Ile-Lys, effectively alleviates translational arrest caused by WPPP. Moreover, the proximity between SKIK and WPPP significantly influences the extent of this alleviation, observed in both PURE cell-free protein synthesis and in vivo protein production systems, resulting in a substantial increase in the yield of proteins containing such RAPs.

Denoising cosine similarity: A theory-driven approach for efficient representation learning.

Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstream tasks efficiently. However, recent works pay little attention to the fact that real-world datasets used during the stage of representation learning are commonly contaminated by noise, which can degrade the quality of learned representations. This paper tackles the problem to learn robust representations against noise in a raw dataset.